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Normalizing Flows Are Capable Generative Models
Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density…
They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. We present TarFlow: a simple and scalable architecture that enables highly performant NF models. Putting these together, TarFlow sets new state-of-the-art results on likelihood estimation for images, beating the previous best methods by a large margin, and generates samples with quality and diversity comparable to diffusion models, for the first time with a stand-alone NF model.
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